数据分析是指用适当的统计方法对收集来的大量第一手资料和第二手资料进行分析,以求最大化地开发数据资料的功能,发挥数据的作用。

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本书通过使用Python的案例研究来探索数据分析和统计的基础知识。这本书将向你展示如何自信地用Python编写代码,以及如何使用各种Python库和函数来分析任何数据集。该代码在Jupyter 笔记本中提出,可以进一步调整和扩展。

这本书分为三个部分——用Python编程,数据分析和可视化,以及统计。首先介绍Python——语法、函数、条件语句、数据类型和不同类型的容器。然后,您将回顾更高级的概念,如正则表达式、文件处理和用Python解决数学问题。

本书的第二部分将介绍用于数据分析的Python库。将有一个介绍性的章节涵盖基本概念和术语,和一个章节的NumPy(科学计算库),NumPy(数据角力库)和可视化库,如Matplotlib和Seaborn。案例研究将包括作为例子,以帮助读者理解一些实际应用的数据分析。

本书的最后几章集中在统计学上,阐明了与数据科学相关的统计学的重要原则。这些主题包括概率、贝叶斯定理、排列和组合、假设检验(方差分析、卡方检验、z检验和t检验),以及Scipy库如何简化涉及统计的繁琐计算。

你会: 进一步提高你的Python编程和分析技能 用Python解决微积分、集合论和代数中的数学问题 使用Python中的各种库来结构化、分析和可视化数据 使用Python进行实际案例研究 回顾基本的统计概念,并使用Scipy库来解决统计方面的问题

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This study presents survey results of the public's willingness to get vaccinated against COVID-19 during an early phase of the pandemic and examines factors that could influence vaccine acceptance based on a between-subjects design. A representative quota sample of 572 adults in the US and UK participated in an online survey. First, the participants' medical use tendencies and initial vaccine acceptance were assessed; then, short vignettes were provided to evaluate their changes in attitude towards COVID-19 vaccines. For data analysis, ANOVA and post hoc pairwise comparisons were used. The participants were more reluctant to vaccinate their children than themselves and the elderly. The use of artificial intelligence (AI) in vaccine development did not influence vaccine acceptance. Vignettes that explicitly stated the high effectiveness of vaccines led to an increase in vaccine acceptance. Our study suggests public policies emphasizing the vaccine effectiveness against the virus could lead to higher vaccination rates. We also discuss the public's expectations of governments concerning vaccine safety and present a series of implications based on our findings.

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